94 lines
4.3 KiB
Python
94 lines
4.3 KiB
Python
import os
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import time
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import random
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import torch
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from torch import nn
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from utils.checkpoints import load_model
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from models.base import AEEncoder
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from models.pyramid_level import PyramidLevel
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from torchvision.transforms import Resize
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from torchvision.transforms.functional import InterpolationMode
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class Pyramid(nn.Module):
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def __init__(self, config):
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super(Pyramid, self).__init__()
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self.static_enc : AEEncoder = AEEncoder(config['cond_ae_static']['encoder_block_settings'], kernel_size=config['cond_ae_static']['kernel_size'], activation=config['cond_ae_static']['activation'])
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if config['static_model'] != '':
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load_model(self.static_enc, config['static_model'] + '_enc')
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self.body_enc : AEEncoder = AEEncoder(config['cond_ae_body']['encoder_block_settings'], kernel_size=config['cond_ae_body']['kernel_size'], activation=config['cond_ae_body']['activation'])
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if config['body_model'] != '':
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load_model(self.body_enc, config['body_model'] + '_enc')
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if config['use_normals']:
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self.norm_enc : AEEncoder = AEEncoder(config['cond_ae_norm']['encoder_block_settings'], config['cond_ae_norm']['kernel_size'], config['cond_ae_norm']['activation'], device=config['device'])
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if config['normals_model'] != '':
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load_model(self.norm_enc, config['normals_model'] + '_enc')
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if config['freeze_encoders']:
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for param in self.static_enc.parameters(): param.requires_grad = False
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for param in self.body_enc.parameters(): param.requires_grad = False
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if config['use_normals']:
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for param in self.norm_enc.parameters(): param.requires_grad = False
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self.levels = nn.ModuleList([PyramidLevel({ **config['pyramid']['levels'][i] }, use_normals=config['use_normals']) for i in range(len(config['pyramid']['levels']))])
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self.resizers = { res: Resize(size=(res, int(res/2)), interpolation=InterpolationMode.BILINEAR).to(config['device']) for res in config['pyramid']['resolutions'] }
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self.resizers[512] = Resize(size=(512, 256), interpolation=InterpolationMode.BILINEAR).to(config['device'])
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self.use_normals = config['use_normals']
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def encode_conds(self, uv_template, uv_body_posed, uv_norm):
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z_static = self.static_enc(uv_template)
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z_body = self.body_enc(uv_body_posed)
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if self.use_normals:
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z_norm = self.norm_enc(uv_norm)
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z_cond = torch.concat([z_static, z_body, z_norm], dim=1)
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else:
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z_cond = torch.concat([z_static, z_body], dim=1)
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return z_cond
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def forward(self, inputs):
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levels_input, conds = inputs
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uv_static, uv_body, uv_norm = conds[0], conds[1], conds[2]
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z_cond = self.encode_conds(uv_static, uv_body, uv_norm)
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out_levels = []
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pred, mean, logvar = self.levels[0](levels_input[0], z_cond)
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# if self.use_offsets:
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# pred = pred + levels_input[0][2] # uv_body_posed_32
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if 1 != len(self.levels):
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pred = self.levels[0].enhance(pred, levels_input[0][-2]) # levels_input[i][-2]=uv_mask_res
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pred = self.resizers[self.levels[1].size](pred)
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out_levels.append([pred, mean, logvar])
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pred = pred * levels_input[1][-2][:,None,:,:]
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else:
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pred = self.levels[0].enhance(pred, levels_input[0][-2]) # levels_input[i][-2]=uv_mask_res
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pred = self.resizers[512](pred)
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pred = pred * levels_input[-1][-1][:,None,:,:] # full res uv mask
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out_levels.append([pred, mean, logvar])
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for i in range(1, len(self.levels)):
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input = levels_input[i]
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input[0] = input[0] - pred
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pred_level, mean, logvar = self.levels[i](input, z_cond)
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pred = pred + pred_level
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if i != len(self.levels) - 1:
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pred = self.levels[i].enhance(pred, levels_input[i][-2])
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out_levels.append([pred, mean, logvar])
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pred = self.resizers[self.levels[i+1].size](pred)
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pred = pred * levels_input[i+1][-2][:,None,:,:]
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else:
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pred = self.levels[-1].enhance(pred, levels_input[-1][-2])
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pred = self.resizers[512](pred)
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pred = pred * levels_input[-1][-1][:,None,:,:] # full res uv mask
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out_levels.append([pred, mean, logvar])
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return out_levels |